Overview

Dataset statistics

Number of variables10
Number of observations43152
Missing cells0
Missing cells (%)0.0%
Duplicate rows92
Duplicate rows (%)0.2%
Total size in memory3.1 MiB
Average record size in memory76.0 B

Variable types

Numeric9
Categorical1

Alerts

Dataset has 92 (0.2%) duplicate rowsDuplicates
carat is highly overall correlated with price and 3 other fieldsHigh correlation
price is highly overall correlated with carat and 3 other fieldsHigh correlation
x is highly overall correlated with carat and 3 other fieldsHigh correlation
y is highly overall correlated with carat and 3 other fieldsHigh correlation
z is highly overall correlated with carat and 3 other fieldsHigh correlation
color has 5413 (12.5%) zerosZeros
clarity has 585 (1.4%) zerosZeros

Reproduction

Analysis started2023-12-03 10:10:58.023040
Analysis finished2023-12-03 10:11:10.074831
Duration12.05 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

carat
Real number (ℝ)

HIGH CORRELATION 

Distinct268
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79823322
Minimum0.2
Maximum5.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size674.2 KiB
2023-12-03T11:11:10.208660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.4
median0.7
Q31.04
95-th percentile1.7
Maximum5.01
Range4.81
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation0.47334152
Coefficient of variation (CV)0.59298649
Kurtosis1.2331988
Mean0.79823322
Median Absolute Deviation (MAD)0.32
Skewness1.1130548
Sum34445.36
Variance0.22405219
MonotonicityNot monotonic
2023-12-03T11:11:10.390756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 2039
 
4.7%
1.01 1792
 
4.2%
0.31 1783
 
4.1%
0.7 1606
 
3.7%
0.32 1487
 
3.4%
1 1213
 
2.8%
0.9 1189
 
2.8%
0.41 1098
 
2.5%
0.71 1044
 
2.4%
0.4 1044
 
2.4%
Other values (258) 28857
66.9%
ValueCountFrequency (%)
0.2 10
 
< 0.1%
0.21 8
 
< 0.1%
0.22 5
 
< 0.1%
0.23 226
0.5%
0.24 200
0.5%
0.25 159
0.4%
0.26 200
0.5%
0.27 195
0.5%
0.28 158
0.4%
0.29 99
0.2%
ValueCountFrequency (%)
5.01 1
< 0.1%
4.5 1
< 0.1%
4.13 1
< 0.1%
3.67 1
< 0.1%
3.65 1
< 0.1%
3.4 1
< 0.1%
3.24 1
< 0.1%
3.22 1
< 0.1%
3.11 1
< 0.1%
3.05 1
< 0.1%

color
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5910734
Minimum0
Maximum6
Zeros5413
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size505.7 KiB
2023-12-03T11:11:10.542755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6987569
Coefficient of variation (CV)0.65561899
Kurtosis-0.86308919
Mean2.5910734
Median Absolute Deviation (MAD)1
Skewness0.19045573
Sum111810
Variance2.8857751
MonotonicityNot monotonic
2023-12-03T11:11:10.670132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 9015
20.9%
1 7863
18.2%
2 7644
17.7%
4 6707
15.5%
0 5413
12.5%
5 4274
9.9%
6 2236
 
5.2%
ValueCountFrequency (%)
0 5413
12.5%
1 7863
18.2%
2 7644
17.7%
3 9015
20.9%
4 6707
15.5%
5 4274
9.9%
6 2236
 
5.2%
ValueCountFrequency (%)
6 2236
 
5.2%
5 4274
9.9%
4 6707
15.5%
3 9015
20.9%
2 7644
17.7%
1 7863
18.2%
0 5413
12.5%

clarity
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8340054
Minimum0
Maximum7
Zeros585
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size505.7 KiB
2023-12-03T11:11:10.974133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7249381
Coefficient of variation (CV)0.44990498
Kurtosis-0.82615679
Mean3.8340054
Median Absolute Deviation (MAD)1
Skewness0.17678979
Sum165445
Variance2.9754115
MonotonicityNot monotonic
2023-12-03T11:11:11.109657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 10520
24.4%
5 9793
22.7%
3 7306
16.9%
4 6530
15.1%
7 4044
 
9.4%
6 2944
 
6.8%
1 1430
 
3.3%
0 585
 
1.4%
ValueCountFrequency (%)
0 585
 
1.4%
1 1430
 
3.3%
2 10520
24.4%
3 7306
16.9%
4 6530
15.1%
5 9793
22.7%
6 2944
 
6.8%
7 4044
 
9.4%
ValueCountFrequency (%)
7 4044
 
9.4%
6 2944
 
6.8%
5 9793
22.7%
4 6530
15.1%
3 7306
16.9%
2 10520
24.4%
1 1430
 
3.3%
0 585
 
1.4%

depth
Real number (ℝ)

Distinct177
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.743046
Minimum43
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size674.2 KiB
2023-12-03T11:11:11.287690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile59.3
Q161
median61.8
Q362.5
95-th percentile63.8
Maximum79
Range36
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.4282431
Coefficient of variation (CV)0.023132048
Kurtosis5.280629
Mean61.743046
Median Absolute Deviation (MAD)0.7
Skewness-0.087590167
Sum2664335.9
Variance2.0398784
MonotonicityNot monotonic
2023-12-03T11:11:11.475728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 1787
 
4.1%
61.9 1739
 
4.0%
61.8 1670
 
3.9%
62.2 1648
 
3.8%
62.1 1613
 
3.7%
61.6 1549
 
3.6%
61.7 1536
 
3.6%
62.3 1518
 
3.5%
62.4 1411
 
3.3%
61.5 1355
 
3.1%
Other values (167) 27326
63.3%
ValueCountFrequency (%)
43 1
< 0.1%
44 1
< 0.1%
51 1
< 0.1%
52.2 1
< 0.1%
52.3 1
< 0.1%
52.7 1
< 0.1%
53 1
< 0.1%
53.1 1
< 0.1%
53.2 2
< 0.1%
53.4 1
< 0.1%
ValueCountFrequency (%)
79 1
< 0.1%
78.2 1
< 0.1%
73.6 1
< 0.1%
72.9 1
< 0.1%
72.2 1
< 0.1%
71.8 1
< 0.1%
71.6 1
< 0.1%
71.3 1
< 0.1%
70.8 2
< 0.1%
70.6 2
< 0.1%

table
Real number (ℝ)

Distinct125
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.459548
Minimum43
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size674.2 KiB
2023-12-03T11:11:11.658727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile54
Q156
median57
Q359
95-th percentile61
Maximum95
Range52
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2293248
Coefficient of variation (CV)0.038798162
Kurtosis3.1564265
Mean57.459548
Median Absolute Deviation (MAD)1
Skewness0.80595361
Sum2479494.4
Variance4.9698891
MonotonicityNot monotonic
2023-12-03T11:11:11.833688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 7942
18.4%
57 7771
18.0%
58 6647
15.4%
59 5298
12.3%
55 4979
11.5%
60 3452
8.0%
54 2061
 
4.8%
61 1812
 
4.2%
62 1000
 
2.3%
63 472
 
1.1%
Other values (115) 1718
 
4.0%
ValueCountFrequency (%)
43 1
 
< 0.1%
44 1
 
< 0.1%
49 1
 
< 0.1%
50 2
 
< 0.1%
50.1 1
 
< 0.1%
51 8
 
< 0.1%
51.6 1
 
< 0.1%
52 40
 
0.1%
52.8 2
 
< 0.1%
53 442
1.0%
ValueCountFrequency (%)
95 1
 
< 0.1%
79 1
 
< 0.1%
76 1
 
< 0.1%
73 2
 
< 0.1%
71 1
 
< 0.1%
70 5
 
< 0.1%
69 7
 
< 0.1%
68 18
 
< 0.1%
67 32
0.1%
66 71
0.2%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct10694
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3939.4907
Minimum326
Maximum18818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size674.2 KiB
2023-12-03T11:11:12.003688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum326
5-th percentile544
Q1956
median2401
Q35354.25
95-th percentile13105.35
Maximum18818
Range18492
Interquartile range (IQR)4398.25

Descriptive statistics

Standard deviation3990.001
Coefficient of variation (CV)1.0128215
Kurtosis2.1498373
Mean3939.4907
Median Absolute Deviation (MAD)1671
Skewness1.6112291
Sum1.699969 × 108
Variance15920108
MonotonicityNot monotonic
2023-12-03T11:11:12.190223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
605 109
 
0.3%
698 101
 
0.2%
789 101
 
0.2%
625 99
 
0.2%
828 99
 
0.2%
544 98
 
0.2%
776 92
 
0.2%
802 92
 
0.2%
552 92
 
0.2%
720 92
 
0.2%
Other values (10684) 42177
97.7%
ValueCountFrequency (%)
326 2
< 0.1%
327 1
< 0.1%
334 1
< 0.1%
336 1
< 0.1%
337 1
< 0.1%
338 1
< 0.1%
339 1
< 0.1%
340 1
< 0.1%
342 1
< 0.1%
344 1
< 0.1%
ValueCountFrequency (%)
18818 1
< 0.1%
18806 1
< 0.1%
18804 1
< 0.1%
18797 1
< 0.1%
18795 2
< 0.1%
18791 1
< 0.1%
18788 1
< 0.1%
18787 1
< 0.1%
18781 1
< 0.1%
18780 1
< 0.1%

x
Real number (ℝ)

HIGH CORRELATION 

Distinct544
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7326071
Minimum0
Maximum10.74
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size674.2 KiB
2023-12-03T11:11:12.375224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.29
Q14.72
median5.7
Q36.54
95-th percentile7.65
Maximum10.74
Range10.74
Interquartile range (IQR)1.82

Descriptive statistics

Standard deviation1.1201963
Coefficient of variation (CV)0.19540783
Kurtosis-0.61092113
Mean5.7326071
Median Absolute Deviation (MAD)0.92
Skewness0.37530417
Sum247373.46
Variance1.2548397
MonotonicityNot monotonic
2023-12-03T11:11:12.566224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.38 359
 
0.8%
4.37 350
 
0.8%
4.34 339
 
0.8%
4.39 324
 
0.8%
4.33 324
 
0.8%
4.32 320
 
0.7%
4.35 313
 
0.7%
4.31 312
 
0.7%
4.41 308
 
0.7%
4.36 301
 
0.7%
Other values (534) 39902
92.5%
ValueCountFrequency (%)
0 7
< 0.1%
3.73 2
 
< 0.1%
3.74 1
 
< 0.1%
3.76 1
 
< 0.1%
3.77 1
 
< 0.1%
3.79 2
 
< 0.1%
3.81 3
< 0.1%
3.82 2
 
< 0.1%
3.83 3
< 0.1%
3.84 3
< 0.1%
ValueCountFrequency (%)
10.74 1
 
< 0.1%
10.23 1
 
< 0.1%
10 1
 
< 0.1%
9.86 1
 
< 0.1%
9.54 1
 
< 0.1%
9.53 1
 
< 0.1%
9.51 1
 
< 0.1%
9.49 1
 
< 0.1%
9.44 3
< 0.1%
9.42 2
< 0.1%

y
Real number (ℝ)

HIGH CORRELATION 

Distinct543
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.736434
Minimum0
Maximum58.9
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size674.2 KiB
2023-12-03T11:11:12.747862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.3
Q14.73
median5.71
Q36.54
95-th percentile7.64
Maximum58.9
Range58.9
Interquartile range (IQR)1.81

Descriptive statistics

Standard deviation1.1474997
Coefficient of variation (CV)0.20003712
Kurtosis112.0351
Mean5.736434
Median Absolute Deviation (MAD)0.92
Skewness2.9098111
Sum247538.6
Variance1.3167556
MonotonicityNot monotonic
2023-12-03T11:11:12.968862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.34 348
 
0.8%
4.37 348
 
0.8%
4.35 331
 
0.8%
4.38 329
 
0.8%
4.33 326
 
0.8%
4.32 323
 
0.7%
4.39 321
 
0.7%
4.4 321
 
0.7%
4.41 308
 
0.7%
4.36 301
 
0.7%
Other values (533) 39896
92.5%
ValueCountFrequency (%)
0 6
< 0.1%
3.68 1
 
< 0.1%
3.71 2
 
< 0.1%
3.72 1
 
< 0.1%
3.73 1
 
< 0.1%
3.75 1
 
< 0.1%
3.77 2
 
< 0.1%
3.78 5
< 0.1%
3.82 1
 
< 0.1%
3.83 1
 
< 0.1%
ValueCountFrequency (%)
58.9 1
< 0.1%
31.8 1
< 0.1%
10.54 1
< 0.1%
10.16 1
< 0.1%
9.85 1
< 0.1%
9.81 1
< 0.1%
9.48 1
< 0.1%
9.46 1
< 0.1%
9.42 1
< 0.1%
9.4 1
< 0.1%

z
Real number (ℝ)

HIGH CORRELATION 

Distinct366
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5392559
Minimum0
Maximum31.8
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size674.2 KiB
2023-12-03T11:11:13.149505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.65
Q12.91
median3.53
Q34.04
95-th percentile4.73
Maximum31.8
Range31.8
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation0.70806215
Coefficient of variation (CV)0.20005961
Kurtosis58.221996
Mean3.5392559
Median Absolute Deviation (MAD)0.57
Skewness1.7929706
Sum152725.97
Variance0.50135201
MonotonicityNot monotonic
2023-12-03T11:11:13.332505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 599
 
1.4%
2.69 596
 
1.4%
2.71 586
 
1.4%
2.68 579
 
1.3%
2.72 575
 
1.3%
2.67 515
 
1.2%
2.73 487
 
1.1%
2.74 438
 
1.0%
2.66 427
 
1.0%
4.02 421
 
1.0%
Other values (356) 37929
87.9%
ValueCountFrequency (%)
0 19
< 0.1%
1.07 1
 
< 0.1%
1.53 1
 
< 0.1%
2.06 1
 
< 0.1%
2.24 1
 
< 0.1%
2.25 1
 
< 0.1%
2.26 1
 
< 0.1%
2.27 1
 
< 0.1%
2.29 1
 
< 0.1%
2.3 1
 
< 0.1%
ValueCountFrequency (%)
31.8 1
< 0.1%
8.06 1
< 0.1%
6.98 1
< 0.1%
6.72 1
< 0.1%
6.43 1
< 0.1%
6.38 1
< 0.1%
6.27 1
< 0.1%
6.13 1
< 0.1%
5.98 1
< 0.1%
5.97 1
< 0.1%

cut
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size674.2 KiB
2
17259 
3
11016 
4
9700 
1
3902 
0
 
1275

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43152
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row3
4th row1
5th row4

Common Values

ValueCountFrequency (%)
2 17259
40.0%
3 11016
25.5%
4 9700
22.5%
1 3902
 
9.0%
0 1275
 
3.0%

Length

2023-12-03T11:11:13.498505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:11:13.655555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 17259
40.0%
3 11016
25.5%
4 9700
22.5%
1 3902
 
9.0%
0 1275
 
3.0%

Most occurring characters

ValueCountFrequency (%)
2 17259
40.0%
3 11016
25.5%
4 9700
22.5%
1 3902
 
9.0%
0 1275
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43152
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 17259
40.0%
3 11016
25.5%
4 9700
22.5%
1 3902
 
9.0%
0 1275
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43152
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 17259
40.0%
3 11016
25.5%
4 9700
22.5%
1 3902
 
9.0%
0 1275
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 17259
40.0%
3 11016
25.5%
4 9700
22.5%
1 3902
 
9.0%
0 1275
 
3.0%

Interactions

2023-12-03T11:11:08.554335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:10:59.090762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:00.280009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:01.433141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:02.597107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:04.004862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:05.136442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:06.320218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:07.440257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:08.674334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:10:59.215656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:00.402005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:01.563143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:02.724132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:04.151864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:05.260038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:06.451265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:07.559256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:08.796365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:10:59.358626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:00.535003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:01.698143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:02.853135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:04.288901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:05.388574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:06.577266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:07.682062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:08.941296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:10:59.486625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:00.661006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:01.838145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:02.985103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:04.414865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:05.521496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:06.710257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:07.807096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:09.072186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:10:59.623665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:00.791020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:01.975143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:03.139102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:04.539866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:05.657497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:06.841258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:07.936394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:09.191378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:10:59.750650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:00.917464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:02.092141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:03.266103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:04.652900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:05.784260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:06.954262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:08.053392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:09.322406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:10:59.906627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:01.051497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:02.225141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:03.403147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:04.779870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:05.923258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:07.081260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:08.185395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:09.439338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:00.028664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:01.178516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:02.344106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:03.540107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:04.896185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:06.051223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:07.198222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:08.307393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:09.563982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:00.152005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:01.307141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:02.469133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:03.857144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:05.016219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:06.180261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:07.318260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-03T11:11:08.430296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-03T11:11:13.779593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
caratclaritycolorcutdepthpricetablexyz
carat1.000-0.2150.2490.1160.0330.9630.1920.9960.9960.993
clarity-0.2151.000-0.0200.143-0.050-0.115-0.084-0.213-0.211-0.216
color0.249-0.0201.0000.0400.0520.1500.0300.2450.2450.250
cut0.1160.1430.0401.000-0.1280.0360.2890.0420.0440.020
depth0.033-0.0500.052-0.1281.0000.013-0.248-0.021-0.0230.106
price0.963-0.1150.1500.0360.0131.0000.1680.9630.9630.957
table0.192-0.0840.0300.289-0.2480.1681.0000.1990.1930.157
x0.996-0.2130.2450.042-0.0210.9630.1991.0000.9980.987
y0.996-0.2110.2450.044-0.0230.9630.1930.9981.0000.987
z0.993-0.2160.2500.0200.1060.9570.1570.9870.9871.000

Missing values

2023-12-03T11:11:09.730686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-03T11:11:09.946565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

caratcolorclaritydepthtablepricexyzcut
265462.012358.164.0162318.238.194.771
91591.011360.060.045406.576.493.924
141311.104562.558.057296.596.544.103
157571.501361.565.063007.217.174.421
246321.523462.157.0129687.277.324.534
498280.563659.759.021675.415.353.213
386820.302661.955.010414.324.342.682
446040.533561.856.016075.215.183.212
114591.153562.254.050086.746.654.172
24950.511661.356.031975.135.213.172
caratcolorclaritydepthtablepricexyzcut
471910.511462.356.018375.145.133.203
219621.525462.959.9100327.277.314.594
371940.465462.356.09744.964.933.082
168501.001562.759.067206.386.313.983
62650.870361.454.040126.156.203.792
112841.055562.459.049756.486.514.054
447320.470461.055.016175.035.013.062
381580.332160.358.010144.494.462.704
8600.906262.859.028716.136.033.823
157951.142260.458.063206.826.794.113

Duplicate rows

Most frequently occurring

caratcolorclaritydepthtablepricexyzcut# duplicates
560.793262.357.028985.905.853.6624
00.300262.258.07094.314.282.6732
10.303162.155.08634.324.352.6922
20.304262.257.04504.274.282.6622
30.306463.457.03944.234.262.6912
40.306463.457.05064.264.232.6942
50.310263.556.05714.294.312.7312
60.310263.556.07324.314.292.7342
70.310562.154.07344.334.372.7022
80.310562.154.09424.374.332.7022